Journal Article

UUV-Assisted Icebreaking Application in Polar Environments Using GA-SPSO

Wang, Yang, Pan, Wei, Peng, Likun, Zhang, Xiaofeng, Song, Fei

2024

Overview

This paper addresses the challenges faced by icebreakers in polar environments, particularly the difficulty in sensing underwater ice formations when navigating through thick ice layers, which often results in suboptimal icebreaking effectiveness. To overcome these challenges, this paper introduces a novel underwater robot equipped with both sensing and icebreaking capabilities. We propose a path-planning method for icebreaking that leverages the synergistic capabilities of the genetic algorithm and safe particle swarm optimization (GA-SPSO). The GA-SPSO algorithm integrates the global search prowess of the particle swarm optimization with the local optimization strength of the genetic algorithm, enabling efficient and adaptive path planning in complex ice environments. The unmanned underwater vehicles (UUV)-assisted icebreaking approach developed here utilizes the UUV’s flexibility and high-precision environmental sensing to provide real-time optimization suggestions for icebreaker navigation paths. Simulation results demonstrate that the GA-SPSO algorithm not only effectively circumvents hazardous areas but also significantly reduces the energy consumption and operational time of icebreakers, thereby enhancing the safety and stability of navigation. When compared to the conventional safe particle swarm optimization (SPSO), our approach shows marked improvements in path length, convergence speed, and obstacle avoidance capabilities, significantly enhancing the success and efficiency of polar navigation missions.

• Publisher: MDPI AG
• Subject: Adaptability / Adaptive algorithms / Algorithms / Artificial intelligence / Autonomous underwater vehicles / Climate change / Design / Efficiency / Energy consumption / genetic algorithm (GA) / Genetic algorithms / Hazardous areas / Ice / Ice environments / ice layer sensing / Ice thickness / Icebreakers / Local optimization / Mathematical optimization / Navigation / Navigation systems / Obstacle avoidance / Optimization / Particle swarm optimization / Path planning / Planning / Polar environments / Polar navigation / Polar regions / Real time / Robots / safe particle swarm optimization (SPSO) / Underwater robots / Underwater vehicles / Unmanned aerial vehicles / Unmanned underwater vehicles / unmanned underwater vehicles (UUV)